Systems and methods for real-time user verification in online education

ABSTRACT

Systems and methods for real-time user verification in online education are disclosed. In certain example embodiments, user identifying information associated with a user and a request to access online education content may be received from a user device. A face template including historical facial image data for the user can be identified. Current facial image data can be compared to the face template to determine if a match exists. Biometric sensor data, such as heart rate data, may also be received for the user. The biometric sensor data may be evaluated to determine if the user is currently located at the user device. If the user is currently located at the user device and the current facial image data matches the face template, access to the online education content may be provided to the user at the user device.

TECHNICAL FIELD

This disclosure generally relates to online education and morespecifically to systems and methods for real-time user verification inan online education environment.

BACKGROUND

The educational framework is changing. Over the last two decades, therehas been a significant increase in online educational offerings,including from traditional educational institutions. Despite theincrease in online educational offerings, problems still persist. One ofthe main issues that continues to prevent online education from having asimilar stature as traditional brick-and-mortar schools is the potentialfor fraud. More specifically, current educational providers that provideonline offerings have a difficult time verifying that the person takingan online course is who he/she says they are. This potential for fraudreduces the perceived stature and value of an online education ascompared to a traditional “in-person” education.

BRIEF DESCRIPTION OF THE FIGURES

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a simplified block diagram illustrating an example environmentincluding online students and education servers providing real-time userverification in an online education environment, in accordance withexample embodiments of the disclosure.

FIGS. 2A and 2B are simplified block diagrams illustrating an examplearchitecture of user devices that provide user images and/or biometricuser data for real-time user verification, in accordance with exampleembodiments of the disclosure.

FIG. 3 is a simplified block diagram illustrating an examplearchitecture of an online education server, in accordance with exampleembodiments of the disclosure.

FIG. 4 is a flow chart illustrating an example method for receiving andstoring user-specific image and/or biometric data for use in real-timeuser verification, in accordance with example embodiments of thedisclosure.

FIGS. 5A and 5B are a flow chart illustrating an example method forcontinuous user verification in an online education environment, inaccordance with example embodiments of the disclosure.

FIG. 6 is a flow chart illustrating an example method for predictiveanalysis of user success in an online education environment, inaccordance with certain example embodiments of the disclosure.

FIG. 7 is a flow chart illustrating an example method for determiningwhen to conduct user verification in an online education environment, inaccordance with certain example embodiments of the disclosure.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Embodiments of the disclosure are described more fully hereinafter withreference to the accompanying drawings, in which example embodiments ofthe disclosure are shown. This disclosure may, however, be embodied inmany different forms and should not be construed as limited to theexample embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art.Like numbers refer to like, but not necessarily the same or identical,elements throughout.

Embodiments of the disclosure may provide systems and methods forreal-time user verification based at least in part on images, biometricsensor information, and device identification information from a varietyof sources. Examples of images that may be evaluated may include, butare not limited to, facial images of the user and images of the devices,such as the user device or biometric data device, in use by the userduring an online education session. Examples of the biometric sensorinformation that may be received and evaluated during an onlineeducation session include, but are not limited to, heart rate,fingerprint identification, voice recognition, conductivity of theuser's skin, chemical make-up of the user's sweat, thermal imaging offace veins, near-infrared or infrared imaging, and user hair follicles.This biometric sensor information may be collected by one or morebiometric data devices. Examples of biometric data devices may include,but are not limited to ear buds, headphones, user-wearable biometricsensors, other forms of biometric sensors, or the like. In certainexample embodiments, each biometric data device may further include apattern along an exterior of the biometric data device. The pattern maybe one that is detectable as by a camera associated with the user deviceand can be evaluated by a facial recognition module (discussed below) todetermine if the pattern is an expected pattern associated with theparticular biometric data device associated with the user.

While the example embodiments described below will be described withreference to the biometric sensor information being user heart ratedata, any other biometric sensor and biometric sensor information knownto one of ordinary skill in the art could be substituted for, and shouldeach be individually read as being a part of these systems and methods.As such, where the discussion of the systems and methods below and thedrawings describe ear buds containing a heart rate monitor, any othertype of biometric sensor (including other forms of heart rate monitors)could be substituted and is included as part of this disclosure. Theimages, biometric sensor information, and/or device identificationinformation may be stored and evaluated locally at a user device, and/orreceived and evaluated by one or more education servers from a user viahis/her user device. These user devices may include a variety ofpersonal devices such as communications devices that may include, forexample, a personal computer, a laptop computer, a tablet computingdevice, netbook computers, smart phones, personal digital assistants, orthe like.

The user devices may include or may be communicably coupled to a cameraor other image sensor to capture images of the user and/or the patternson the biometric data devices. In certain example embodiments, thecamera may be embedded within the user device. Alternatively, the cameramay be a separate device that is communicably coupled to the userdevice. In certain example embodiments, the camera may provide standardor infrared imaging for evaluation. The images generated by the cameramay be used to generate a face template for a user. The images generatedby the camera may further be used to generate facial image data of theuser that may be compared to the face template of the user to determineif the facial image data of the user matches the face template for theuser. The images generated by the camera may further be used to identifyone or more known patterns on the biometric data device. Providing knownpatterns on the biometric data device, and using the camera to determineif the known patterns can be detected from the biometric data device,further acts as a spoof deterrent.

The image data, biometric sensor information, and device identificationinformation associated with communications between a user device andcommunications infrastructure, such as a cellular telephone tower, maybe received by the education server. This information may be receiveddirectly from the user device or, alternatively, from a communicationsserver associated with the communications infrastructure. Thisinformation may be used to determine if the user associated with thelogin information at the user device is who he/she says they are and ifthe user is continuing to be physically present at the user device whileonline education services are being provided to the user. For example,the user device and/or the education server may receive image data,biometric sensor information, and device identification information fromthe user device, the camera, and/or the biometric data device. The userdevice and/or the education server can determine if the received deviceidentification information is for a user device and/or biometric datadevice associated with the user, if the image received from the cameramatches a face template for the user, if the image received from thecamera includes a known pattern on the biometric data device, and if thebiometric sensor information indicates the user is actually at the userdevice. Based on one or more of these determinations, the educationserver can determine whether to provide the user access to the desiredonline education services and/or to generate notifications regarding theperceived authenticity of the user at the user device.

FIG. 1 is a simplified block diagram illustrating an example environment100 including online users (e.g., students) and education serversproviding real-time user verification in an online education environment100, in accordance with example embodiments of the disclosure. Theexample online education environment 100 may include one or more user(s)105(1), 105(2), . . . , 105(N) (hereinafter collectively or individuallyreferred to as user 105) using their respective user devices 120(1),120(2), . . . , 120(N) (hereinafter collectively or individuallyreferred to as user device 120).

The user device 120 may be any one of suitable devices that may beconfigured to execute one or more applications, software, and/orinstructions to provide one or more images, sensor signals, and/orcommunications signals. The user device 120, as used herein, may be anyvariety of client devices, electronic devices, communications devices,and/or mobile devices. The user device 120 may include, but is notlimited to, a tablet computing device, an electronic book (ebook)reader, a netbook computer, a notebook computer, a laptop computer, adesktop computer, a web-enabled television, a video game console, apersonal digital assistant (PDA), a smart phone, or the like. While thedrawings and/or specification may portray the user device 120 in thelikeness of a laptop computer, a desktop computer, or a tablet computerdevice, the disclosure is not limited to such. Indeed, the systems andmethods described herein may apply to any electronic device 120generating an image, sensor signal, and/or communication signal.

As depicted in FIG. 1, the example online educational environment 100may include the user device 120 communicably coupled to the educationservers 110 via a network 130. The user device 120 may further becommunicably coupled to a communications infrastructure 140, such as acellular communications tower/receiver. The education servers 110 areconfigured to receive one or more of the images, biometric sensorinformation, and/or device identification information from the userdevices 120 or to facilitate the evaluation of one or more of theimages, biometric sensor information, and/or device identificationinformation by the user devices 120. Based, at least in part, on thereceived images, biometric sensor information, and/or deviceidentification information, the education servers 110 may be configuredto perform facial recognition matching, pattern recognition matching,biometric data evaluation, and/or device matching.

The networks 130 may include any one or a combination of different typesof suitable communications networks, such as cable networks, theInternet, wireless networks, cellular networks, and other private and/orpublic networks. Furthermore the networks 130 may include any variety ofmedium over which network traffic is carried including, but not limitedto, coaxial cable, twisted wire pair, optical fiber, hybrid fibercoaxial (HFC), microwave terrestrial transceivers, radio frequencycommunications, satellite communications, or combinations thereof. It isalso noted that the described techniques may apply in otherclient/server arrangements (e.g., set-top boxes, etc.), as well as innon-client/server arrangements (e.g., locally stored softwareapplications, etc.).

The communications infrastructure 140 may be configured to communicatewith other communications infrastructure and/or user devices 120 usingany suitable communication formats and/or protocols including, but notlimited to, Wi-Fi, direct Wi-Fi, Bluetooth, 3G mobile communication, 4Gmobile communication, long-term evolution (LTE), WiMax, direct satellitecommunications, or any combinations thereof. The communicationsinfrastructure 140 may communicate with other communicationsinfrastructure to receive and then retransmit information, such as datapackets. The communications infrastructure 140 may be configured toreceive wireless communications signals from the user devices 120. Thesecommunications signals may be wireless signals that include images,biometric sensor information, and/or device identification informationfrom the user device 120 carried thereon. These transmitted images,biometric sensor information, and/or device identification informationmay be data that is identified by the user device 120 and coded on toand carried by the wireless signal that is received at thecommunications infrastructure 140. The communications infrastructure 140may further be configured to transmit communications signals to the userdevice 120, such as from the education server 110 via the network 130.

FIGS. 2A and 2B are simplified block diagrams illustrating an examplearchitecture of a user device 120 that provides images, biometric sensorinformation, and/or device identification information for real-time userverification in an online education environment, in accordance withexample embodiments of the disclosure. The user device 120 may includeone or more user interfaces 202, an antenna 204 communicably coupled tothe user device 120, and a camera 206 communicably coupled to the userdevice 120. In certain example embodiments, a biometric data device 208may be communicably coupled to the user device 120. In one exampleembodiment, the biometric data device is ear buds or other types ofheadphones. Alternatively, other forms of biometric data devices may besubstituted. The biometric data device or ear buds 208 may include atleast one known pattern 210 provided on an exterior surface of the earbuds 208. The known pattern 210 may be viewed and recognized by thefacial recognition modules discussed below.

The biometric data device may further include a heart rate monitor 212.In one example embodiment, the heart rate monitor 212 may beincorporated into an ear bud that is placed by the user 105 into theuser's ear and receives and transmits heart rate data from the user 105.The user device 120 may include one or more processor(s) 220,input/output (I/O) interface(s) 222, a radio 224, network interface(s)226, and a memory 230.

The processors 220 of the user device 120 may be implemented asappropriate in hardware, software, firmware, or combinations thereof.Software or firmware implementations of the processors 220 may includecomputer-executable or machine-executable instructions written in anysuitable programming language to perform the various functionsdescribed. Hardware implementations of the processors 220 may beconfigured to execute computer-executable or machine-executableinstructions to perform the various functions described. In exampleembodiments, the processors 220 may be configured to executeinstructions, software, and/or applications stored in the memory 230.The one or more processors 220 may include, without limitation, acentral processing unit (CPU), a digital signal processor (DSP), areduced instruction set computer (RISC), a complex instruction setcomputer (CISC), a System-on-a-Chip (SoC), a microprocessor, amicrocontroller, a field programmable gate array (FPGA), or anycombination thereof. The user device 120 may also include a chipset (notshown) for controlling communications between one or more processors 220and one or more of the other components of the user device 120. Theprocessors 220 may also include one or more application specificintegrated circuits (ASICs) a System-on-a-Chip (SoC), or applicationspecific standard products (ASSPs) for handling specific data processingfunctions or tasks. In certain embodiments, the user device 120 may bebased on an Intel® Architecture system and the one or more processors220 and chipsets may be from a family of Intel® processors and chipsets,such as the Intel® Atom® processor family.

The one or more I/O interfaces 222 may enable the use of one or more I/Odevice(s) or user interface(s), such as a touch sensitive screen, akeyboard, and/or a mouse. The user 105 may be able to administer images,biometric sensor information, and/or device identification informationfrom the user device 120 by interacting with the user interfaces via theI/O interfaces 222. The network interfaces(s) 226 may allow the userdevices 120 to communicate via the one or more network(s) 130 and/or viaother suitable communicative channels. For example, the user device 120may be configured to communicate with stored databases, other computingdevices or servers, user terminals, or other devices on the networks130.

The radio 224 may include any suitable radio for transmitting and/orreceiving radio frequency (RF) signals in the bandwidth and/or channelscorresponding to the communications protocols utilized by the userdevice 120 to communicate with other user devices 120 and/or thecommunications infrastructure 140. The radio 224 may include hardwareand/or software to modulate communications signals according topre-established transmission protocols. The radio 224 may be configuredto generate communications signals for one or more communicationsprotocols including, but not limited to, Wi-Fi, direct Wi-Fi, Bluetooth,3G mobile communication, 4G mobile communication, long-term evolution(LTE), WiMax, direct satellite communications, or combinations thereof.In alternative embodiments, protocols may be used for communicationsbetween a relatively adjacent user device 120 and/or a biometric datadevice 208, such as Bluetooth, dedicated short-range communication(DSRC), or other packetized radio communications. The radio 224 mayinclude any known receiver and baseband suitable for communicating viathe communications protocols of the user device 120. The radio 224 mayfurther include a low noise amplifier (LNA), additional signalamplifiers, an analog-to-digital (A/D) converter, one or more buffers,and a digital baseband. In certain embodiments, the communicationssignals generated by the radio 224 may be transmitted via the antenna204 on the user device 120.

The memory 230 may include one or more volatile and/or non-volatilememory devices including, but not limited to, magnetic storage devices,read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM),static RAM (SRAM), synchronous dynamic RAM (SDRAM), double data rate(DDR) SDRAM (DDR-SDRAM), RAM-BUS DRAM (RDRAM), flash memory devices,electrically erasable programmable read-only memory (EEPROM),non-volatile RAM (NVRAM), universal serial bus (USB) removable memory,or combinations thereof.

The memory 230 may store program instructions that are loadable andexecutable on the processor(s) 220, as well as data generated orreceived during the execution of these programs. The memory 230 may havestored thereon software modules including an operating system (O/S)module 232, an applications module 234, a communications module 236, aface recognition module 238, a bio sensors module 240, user data files242, and a user verification module 252. Each of the modules, files,and/or software stored on the memory 230 may provide functionality forthe user device 120, when executed by the processors 220.

The O/S module 232 may have one or more operating systems storedthereon. The processors 220 may be configured to access and execute oneor more operating systems stored in the O/S module 232 to operate thesystem functions of the user device 120. System functions, as managed bythe operating system, may include memory management, processor resourcemanagement, driver management, application software management, systemconfiguration, and the like. The operating system may be any variety ofsuitable operating systems including, but not limited to, Google®Android®, Microsoft® Windows®, Microsoft® Windows® Server®, Linux,Apple® OS-X®, or the like.

The applications module 234 may contain instructions and/or applicationsthereon that may be executed by the processors 220 to provide one ormore functionalities associated with the user device 120. Theseinstructions and/or applications may, in certain aspects, interact withthe O/S module 232 and/or other modules of the user device 120. Theapplications module 234 may have instructions, software, and/or codestored thereon that may be launched and/or executed by the processors220 to execute one or more applications and functionality associatedtherewith. These applications may include, but are not limited to,functionality such as web browsing, business, communications, graphics,word processing, publishing, spreadsheets, databases, gaming, education,entertainment, media, project planning, engineering, drawing, orcombinations thereof.

The communications module 236 may have instructions stored thereon that,when executed by the processors 220, enable the user device 120 toprovide a variety of communications functionality. In one aspect, theprocessors 220, by executing instructions stored in the communicationsmodule 236, may be configured to demodulate and/or decode communicationssignals received by the user device 120 via the antenna 204 and theradio 224. The received communications signals may further carry audio,beacon data, handshaking, information, and/or other data thereon. Inanother aspect, the processors 220, by executing instructions from atleast the communications module 236, may be configured to generate andtransmit communications signals via the radio 224 and/or the antenna204. The processors 220 may encode and/or modulate communicationssignals to be transmitted by the user device 120.

The face recognition module 238 may have instructions stored thereonthat, when executed by the processors 220 enable the user device 120 toemploy one or more facial recognition algorithms to compare one imagegenerated by a camera 206 to another image generated by the camera 206to determine if the images match or substantially match. For example,the face recognition module 238 may include instructions for comparingcurrent facial images of the user 105 to a historical face template forthe user 105 to determine if they match or substantially match. Inaddition, the face recognition module 238 may have instructions fordetermining the location of the biometric data device(s) 208 in thecurrent facial images of the user 105, focusing in on the location ofthe biometric data device(s) 208, and determining if the biometric datadevice(s) 208 includes a known pattern 210 on the exterior of thebiometric data device(s) 208.

The biosensors module 240 may have instructions stored thereon that,when executed by the processors 220 enable the user device 120 toreceive and evaluate biometric sensor data from a biometric data device208, such as the ear buds 208 of FIG. 2. In certain example embodiments,the biosensors module 240 may store and access, from the user data files242, historical biometric sensor data for the user 105. The biosensorsmodule 240 may also receive biometric sensor data from the biometricdata device 208, and can determine if the biometric sensor data isindicative of a live person at the user device 120. Further, thebio-sensors module 240 may compare the current biometric sensor data tohistorical biometric sensor data for the user 105 to determine if thedata likely came from the same user 105. For example, the biosensorsmodule 240 may compare the current heart rate data from the user 105 tostored historical heart rate data for the user 105 to determine if thereis a match or substantial match.

The user verification module 252 may have instructions stored thereonthat, when executed by the processors 220 enable the user device 120 toconduct user authorization and evaluation in an online educationenvironment. The user verification module 252 may evaluate thelikelihood that the user 105 is who the user purports to be and, basedon that evaluation, may allow or deny access to the online educationenvironment. Further, the user verification module 252, when executed bythe processors 220, can periodically supplement its verification reviewby conducting additional verification reviews of the user 105. Theverification reviews may include device verification, facialrecognition, biometric data evaluations and comparisons, and evaluationof user scores in online class environments.

The user data files 242 may include information associated with one ormore users 105 (e.g., in situations where multiple users 105 of the userdevice 120 are accessing the online education environment) having accessto the online education environment 100. The user data files 242 mayinclude user identification information (e.g., user name, address, phonenumber, email address, login and password information) for the user 105.The user data files 242 may also include face template files 246, deviceID files 248, and user heart rate data files 250. The face templatefiles 246 may include a face template for the user 105 that may be usedby the face recognition module 238 to compare to the current facialimage data for the user 105 and to verify that the user 105 isauthentic. The device ID files 248 may include the device identificationdata for the user device 120 and/or for any device associated with theuser 105 in the online education environment. The user heart rate datafiles 250 may store historical heart rate data for the user 105. Thehistorical heart rate data may be retrieved and compared by thebio-sensors module 240 to determine if the historical heart rate datamatches or substantially matches the current heart rate data receivedfrom the biometric data device 208.

It will be appreciated that there may be overlap in the functionality ofthe instructions stored in the operating system (O/S) module 232, theapplications module 234, the communications module 236, the facerecognition module 238, the biosensors module 240, and the userverification module 252. In fact, the functions of the aforementionedmodules 232, 234, 236, 238, 240, and 252 may interact and cooperateseamlessly under the framework of the education servers 110. Indeed,each of the functions described for any of the modules 232, 234, 236,238, 240, and 252 may be stored in any module 232, 234, 236, 238, 240,and 252 in accordance with certain embodiments of the disclosure.Further, in certain example embodiments, there may be one single modulethat includes the instructions, programs, and/or applications describedwithin the operating system (O/S) module 232, the applications module234, the communications module 236, the face recognition module 238, thebiosensors module 240, and the user verification module 252.

FIG. 3 is a simplified block diagram illustrating an examplearchitecture of the education server 110, in accordance with exampleembodiments of the disclosure. The education server 110 may include oneor more processors 300, I/O interface(s) 302, network interface(s) 304,storage interface(s) 306, and a memory 310.

In some examples, the processors 300 of the education servers 110 may beimplemented as appropriate in hardware, software, firmware, orcombinations thereof. Software or firmware implementations of theprocessors 300 may include computer-executable or machine-executableinstructions written in any suitable programming language to perform thevarious functions described. Hardware implementations of the processors300 may be configured to execute computer-executable ormachine-executable instructions to perform the various functionsdescribed. The one or more processors 300 may include, withoutlimitation, a central processing unit (CPU), a digital signal processor(DSP), a reduced instruction set computer (RISC), a complex instructionset computer (CISC), a System-on-a-Chip (SoC), a microprocessor, amicrocontroller, a field programmable gate array (FPGA), or anycombination thereof. The education servers 110 may also include achipset (not shown) for controlling communications between the one ormore processors 300 and one or more of the other components of theeducation servers 110. The one or more processors 300 may also includeone or more application specific integrated circuits (ASICs), aSystem-on-a-Chip (SoC), or application specific standard products(ASSPs) for handling specific data processing functions or tasks. Incertain embodiments, the education servers 110 may be based on an Intel®Architecture system and the one or more processors 300 and chipset maybe from a family of Intel® processors and chipsets, such as the Intel®Atom® processor family.

The one or more I/O device interfaces 302 may enable the use of one ormore I/O device(s) or user interface(s), such as a keyboard and/or amouse. The network interfaces(s) 304 may allow the education servers 110to communicate via the one or more network(s) 130 and/or via othersuitable communicative channels. For example, the education servers 110may be configured to communicate with stored databases, other computingdevices or servers, user terminals, or other devices on the networks130. The storage interface(s) 306 may enable the education servers 110to store information, such as images (e.g., face templates), biometricsensor data (e.g., heart rate data), user data (e.g., studentinformation, logins, and passwords), academic records for a multitude ofusers 105 of the online education environment 100, and/or user deviceand biometric data device identification information in storage devices.

The memory 310 may include one or more volatile and/or non-volatilememory devices including, but not limited to, magnetic storage devices,read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM),static RAM (SRAM), synchronous dynamic RAM (SDRAM), double data rate(DDR) SDRAM (DDR-SDRAM), RAM-BUS DRAM (RDRAM), flash memory devices,electrically erasable programmable read-only memory (EEPROM),non-volatile RAM (NVRAM), universal serial bus (USB) removable memory,or combinations thereof.

The memory 310 may store program instructions that are loadable andexecutable on the processor(s) 300, as well as data generated orreceived during the execution of these programs. Turning to the contentsof the memory 310 in more detail, the memory 310 may include one or moreoperating systems (O/S) 312, an applications module 314, an image module316, a facial recognition module 318, a biosensor module 320, a devicevalidation module 334, a user verification module 336, and/or user data322. Each of the modules, data, and/or software may providefunctionality for the education servers 110, when executed by theprocessors 300. The modules, data, and/or the software may or may notcorrespond to physical locations and/or addresses in the memory 310. Inother words, the contents of each of the modules 312, 314, 316, 318,320, 334, and 336 may not be segregated from each other and may, infact, be stored in at least partially interleaved positions on thememory 310. Further, while the example embodiment in FIG. 3 presents themodules 312, 314, 316, 318, 320, 334, and 336 as being separate, inother example embodiments the operations of these modules may becombined in any manner into fewer than the seven modules presented. Forexample, the operations of the image module 316 and the facialrecognition module 318 may be combined. In another example, all of theoperations of these modules 312, 314, 316, 318, 320, 334, and 336 may becompleted by a single module. Any other combination and consolidation ofthe operations of the modules 312, 314, 316, 318, 320, 334, and 336 arecontemplated herein.

The O/S module 312 may have one or more operating systems storedthereon. The processors 300 may be configured to access and execute oneor more operating systems stored in the O/S module 312 to operate thesystem functions of the education server 110. System functions, asmanaged by the operating system, may include memory management,processor resource management, driver management, application softwaremanagement, system configuration, and the like. The operating system maybe any variety of suitable operating systems including, but not limitedto, Google® Android®, Microsoft® Windows®, Microsoft® Windows® Server®,Linux, Apple® OS-X®, or the like.

The application(s) module 314 may contain instructions and/orapplications thereon that may be executed by the processors 300 toprovide one or more functionalities associated with online educationservices provided to a multitude of users 105 (e.g., students). Theseinstructions and/or applications may, in certain aspects, interact withthe O/S module 312 and/or other modules of the education servers 110.The applications module 314 may have instructions, software, and/or codestored thereon that may be launched and/or executed by the processors300 to execute one or more applications and the functionality associatedtherewith. These applications may include, but are not limited to,functionality such as web browsing, business, communications, graphics,word processing, publishing, spreadsheets, databases, gaming, education,entertainment, media, project planning, engineering, drawing, orcombinations thereof.

The image module 316 may have instructions stored thereon that, whenexecuted by the processors 300, enable the education servers 110 toprovide a variety of imaging management and/or image processing relatedfunctionality. In one aspect, the processors 300 may be configured toreceive one or more images from one or more user devices 120 via thenetworks 130 or other suitable communicative links. These images may bestored on the memory 310 and/or other suitable database(s). The imagesmay further be analyzed by the processors 300 by executing instructionsstored in the facial recognition module 318 and/or the biosensor module320.

The facial recognition module 318 may have instructions stored thereonthat, when executed by the processors 300 enable the education server110 to employ one or more facial recognition algorithms to compare oneimage generated by a camera 206 at a user device 120 to another imagegenerated by the camera 206 to determine if the images match orsubstantially match. The comparison may involve a variety of suitablealgorithms and, in certain example embodiments, may result in aprobability of a match of the feature in the first cluster of pixels inthe first image and the second cluster of pixels in the second image. Insome cases, if the probability of a match is greater than apredetermined threshold level, it may be determined that the feature inthe two images may be a match. In some cases, feature matchingalgorithms of this type, performed by the processors 300, may includedetermining a correlation and/or a cross correlation of a variety ofparameters associated with the images, or portions thereof, such as thecluster of pixels that may be compared in the first image and the secondimage. Example parameters that may be compared across images may includepixel color(s), intensity, brightness, or the like. It will beappreciated that while the localization system and mechanism aredescribed with reference to two images, the systems and the algorithmsmay be extended to any number of received images that are to be comparedand localized. It will further be appreciated that the processors 300may perform a variety of mathematical and/or statistical algorithms toidentify and/or “recognize” features that appear across more than oneimage. The mathematical and/or statistical algorithms may involve avariety of suitable techniques, such as iterative comparisons of imagepixels, or portions thereof, and/or a variety of filtering techniques toisolate particular pixels of an image, such as threshold filtering.

For example, the facial recognition module 318 may include instructionsfor comparing current facial images of the user 105 to a historical facetemplate for the user 105 to determine if they match or substantiallymatch. In addition, the facial recognition module 318 may haveinstructions for determining the location of the biometric datadevice(s) 208 in the current facial images of the user 105. The facialrecognition module 318 may focus in on the location of the biometricdata device(s) 208 and determine if the biometric data device(s) 208includes a known pattern 210 on the exterior of the biometric datadevice(s) 208.

The biosensor module 320 may have instructions stored thereon that, whenexecuted by the processors 300, enable the education server 110 toreceive and evaluate biometric sensor data from a biometric data device208, such as the ear buds 208 of FIG. 2. In certain example embodiments,the biosensor module 320 may store and access, from the user data files322 or the user data files 242 of a user device 120, historicalbiometric sensor data for the user 105. The biosensor module 320 mayalso receive biometric sensor data from the biometric data device 208,and can determine if the biometric sensor data is indicative of a liveperson at the user device 120. Further, the biosensor module 320 maycompare the current biometric sensor data to historical biometric sensordata for the user 105 to determine if the data likely came from the sameuser 105. For example, the bio sensor module 320 may compare the currentheart rate data from the user 105 to stored historical heart rate datafor the user 105 to determine if there is a match or substantial match.

The device validation module 334 may have instructions stored thereonthat, when executed by the processors 300, enable the education server110 to receive device identification information from a user device 120,retrieve stored device identification information associated with theuser 105 from a registered device IDs file 328, and compare the storeddevice identification information to the received device identificationinformation to determine if a match exists. The device validation module334 is capable of determining those devices, including user devices 120and biometric data devices 208, that are associated with a particularuser 105. The device validation module 334 is also capable of obtainingnew device identification information for a new device and associatingthe device identification information with the user data in theregistered device IDs file 328. In addition, the device validationmodule 334 may be capable of identifying a device (such as a user device120 or a biometric data device 208) that is shared between multipleusers 105 so that the user verification module 336 in conjunction withthe facial recognition module 318 and/or the biosensor module 320 maydetermine the particular user 105 currently using the device.

The user verification module 336 may have instructions stored thereonthat, when executed by the processors 300, enable the education server110 to conduct user authorization and evaluation in an online educationenvironment. The user verification module 336 may evaluate thelikelihood the user 105 is who that person purports to be and, based onthat evaluation, may allow or deny access to the online educationenvironment. Further, the user verification module 336, when executed bythe processors 300, can periodically supplement its verification of theuser 105 by conducting additional verification reviews of the user 105.The verification reviews may include device verification, facialrecognition, biometric data evaluations and comparisons, and evaluationof user scores in online class environments.

The user data files 322 may include information associated with one ormore users (e.g., students who have access to the online educationenvironment provided by the education server 110. The user data files322 may include user identification information (e.g., user name,address, phone number, email address, login and password information)for each user 105 having access to the online education environment. Theuser data files 322 may also include face template files 324, historicalheart rate data files 326, registered device ID files 328, and academicrecords files 330. The face templates files 324 may include a facetemplate for each user 105 (e.g., student) having access to the onlineeducation environment. Alternatively, the face templates files 324 maynot include the actual face templates for the users 105 but may insteadinclude tokens representing stored face templates for users stored ontheir respective user devices 120. The face templates may be used by thefacial recognition module 318 to compare to current facial image datafor the user 105 and to verify the user is authentic. The historicalheart rate data files 326 may include historical heart rate data forusers 105 having access to the online education environmentAlternatively, the historical heart rate data files 326 may not includethe actual historical heart rate or other biometric data for the users105 but may instead include tokens representing the historical heartrate data or other biometric data for the users 105 stored on theirrespective user devices 120. The historical heart rate data may beretrieved and compared by the biosensor module 320 to determine if thehistorical heart rate data matches or substantially matches the currentheart rate data received from the biometric data device 208.

The registered device IDs files 328 may include the deviceidentification data for each device (e.g., user device and biometricdata device) associated with the user 105 in the online educationenvironment. The user verification module 336 may employ the devicevalidation module 334 to compare a received device identification datato the device identification data associated with the user 105 andstored in the registered device IDs file 328 to determine if a matchexists and the device currently in use by the user 105 is a device theuser 105 is expected to use.

The academic records file 330 may include the academic records for eachuser and former user having access to the online education environment(e.g., each current and former student of the school). In one exampleembodiment, the data in the academic records file 330 may include useridentification information, quizzes and test scores, classes previouslyand currently being taken by a user, user grades, modules or classsessions in which the user 105 participated, review questions attemptedby the user 105, labs attended by the user 105, study sessions anddiscussion groups attended by the user 105 and the content discussed,and the like.

It will be appreciated that there may be overlap in the functionality ofthe instructions stored in the operating systems (O/S) module 312, theapplications module 314, the image module 316, the facial recognitionmodule 318, the biosensor module 320, the device validation module 334and/or the user verification module 336. In fact, the functions of theaforementioned modules 312, 314, 316, 318, 320, 334, and 336 mayinteract and cooperate seamlessly under the framework of the educationservers 110. Indeed, each of the functions described for any of themodules 312, 314, 316, 318, 320, 334, and 336 may be stored in anymodule 312, 314, 316, 318, 320, 334, and 336 in accordance with certainexample embodiments of the disclosure. Further, in certain embodiments,there may be one single module that includes the instructions, programs,and/or applications described within the operating systems (O/S) module312, the applications module 314, the image module 316, the facialrecognition module 318, the biosensor module 320, the device validationmodule 334 and/or the user verification module 336.

FIG. 4 is a flow chart illustrating an example method 400 for receivingand storing user-specific image, device, and biometric data for use inreal-time user verification, in accordance with certain exampleembodiments of the disclosure. This method 400 may be performed by theeducation servers 110 and the processors 300 thereon. Now referring toFIGS. 1-4, the exemplary method 400 begins at the START block andproceeds to block 402, where the education server 110 receives a requestto register or update user information at a website for an online courseprovider. For example, a user 105, via a user device 120, may access theonline course website associated with the education server 110 via thenetwork 130 to request an opportunity to register or otherwise provideuser verification information.

At block 404, the processor 300 may generate or otherwise retrieve frommemory 310 a registration template for providing user verificationinformation. At block 406, the processor 300 of the education server 110may transmit via the network interface 226 by way of the network 130 fordisplay or otherwise provide access to the registration template by theuser device 120. At block 408, the education server 110 may receive userinformation data via user input at the user device 120. For example, theuser information data may include, but is not limited to, the user'sname, address, contact information, social security number, schoolidentification number, or any other information that uniquely identifiesthe user 105. The processor 300 may facilitate the receipt of the userinformation data, which can then be stored in the memory 310. Forexample, the user information data can be stored in the user data 322.

At block 410, the education server 110, via the online course website,may receive login and password data for the user by way of user input atthe user device 120. For example, via the online course website, theprocessor 300 may receive the login and password data. The processor 300may direct a user setup application 314 to associate the userinformation with the user login information and password data at block412. For example, the application 314 may store in the user data 322,the associated user login and password information with the userinformation data.

At block 414, the education server 110 can receive user deviceidentification information (e.g., a device ID) from one or more userdevices 120 via the network 130. For example, the user deviceidentification information can be one or more pieces of information thatuniquely identify the user device 120 from other user devices. Examplesof types of information that may be used to make up the user deviceidentification information include, but are not limited to, devicepassword, operating system name, operating system version, and operatingsystem manufacturer. Those of ordinary skill in the art will recognizethat other forms of device fingerprinting may be substituted herein aspart of the provision of user device information to the education server110. At block 416, the processor 300 may direct a user setup application314 to associate the user information with the device identificationinformation. For example, at block 418, the application 314 may storethe device identification data with the user information data in theuser data file 322 of the memory 310. Alternatively, the deviceidentification data may be stored on the user device 120. In thisalternative embodiment, the processor 220 may direct a user setupapplication 234, for example, to store the device identification data inthe device ID file 248 of the memory 230. A token associated with thedevice identification data may then be received by the education server110 via the network 130, associated with the user information, andstored in the registered device IDs file 328 of the memory 310.

At block 420, an inquiry is conducted to determine if there is anotherdevice to associate with the user information. Examples of other devicesinclude other user devices 120 (such as a different personal computer, alaptop computer, a tablet, a netbook, etc.) and biometric data devices(e.g., ear buds 208, another heart rate monitor, other biometric datadevices and sensors, a camera, etc.). In one example, the determinationmay be made by the processor 300 based on the devices attached to theuser device 120 or via user input at the user device 120 in response toa request from the online course website. If additional deviceinformation data for another device needs to be associated with theuser, the YES branch is followed to block 414. Otherwise, the NO branchis followed to block 422.

At block 422, a request for the user 105 to create a face template isgenerated for display on the user device 120. In one example, theprocessor 300, via the user verification module 336, can generate oraccess a stored copy of the request from the memory 310 and the networkinterface 304 can transmit the request via the network 130 to the userdevice 120. At block 424, image data for the user can be received fromthe camera 206 of the user device 120. At block 426, a face template canbe generated for the user based on the received user image data. Inexample embodiments where the face template data will not be stored atthe education server 110, the processor 220 may employ the userverification module 252 at the user device to generate the face templatedata. In example embodiments, where the face template data will bestored at the education server 110, the processor 300, via the userverification module 336 may receive the user image data and generate theface template for the user 105.

At block 428, the processor 220 or 300 may employ the user verificationmodules 252 and 336, respectively, to associate the face template datafor the user with the user information. At block 430, the face templatefor the user 105 may be stored locally in the face template file 246 onthe user device 120, and encrypted and stored as a token in the facetemplates file 324 of the education server 110. The process may thencontinue to the END block.

FIGS. 5A and 5B are a flow chart illustrating an example method 500 forcontinuous real-time user verification in an online educationenvironment, in accordance with certain example embodiments of thedisclosure. This method 500 may be performed by the education servers110 and the processors 300 thereon. Now referring to FIGS. 1-3 and5A-5B, the exemplary method 500 begins at the START block and proceedsto block 502, where the education server 110 receives a login requestfrom a user 105 at a user device 120. For example, a user 105, via auser device 120, may access the online course website associated withthe education server 110 via the network 130 to make a login request inorder to access online course information (e.g., an online class, aquiz, a lab, practice questions, a help session, a test, etc.). At block504, login information for the user 105 is received. For example, theuser 105 may input from the user device 120, the login information(e.g., login name and password) in a desired location of the onlinecourse website. The login information may be received by the educationserver 110 via the network 130. At block 506, the processor 300 of theeducation server 110 employs the user verification module 336 todetermine the user associated with the login information. For example,the user verification module 336 can compare the login information withthe login information of multiple users stored in the user data file 322to determine if a match exists and to determine the user based on thematching login information.

At block 508, the processor 300 employs the user verification module 336to access user information data for the matching user 105. For example,the user information data may include the actual device identificationinformation associated with the user 105, the face template for the user105, and the biometric data for the user 105. Alternatively, the userinformation data may include one or more tokens associated with thedevice identification information, the face template, and the biometricdata for the user 105 and the actual data may be stored on the userdevice 120.

At block 510, the education server 110 receives device identificationinformation from the user device 120 via the network 130. In oneexample, the device identification information can be a device ID. Incertain example embodiments, the user login information (e.g., loginname and password) may not be received because it may not be deemednecessary, and identifying the user, the face template for the user,and/or the historical biometric data for the user may be based on anevaluation of the device identification information rather than the userlogin information. As such, a reference to user identifying informationmay include one or more of the device identification information, theuser login information, or any other information that uniquelyidentifies the user 105. At block 512, an inquiry is conducted todetermine if the device identification information matches the storeddevice identification information for the user. The processor 300 of theeducation server 110 can employ the user verification module 336 tocompare the received device identification information to the storeddevice identification information for the user 105 to determine if amatch exists. In alternative embodiments where the evaluation takesplace at the user device 120, the processor 220 of the user device 120can employ the user verification module 252 to compare the currentdevice identification information for the user device 120 to the storeddevice identification information for the user 105 to determine if amatch exists. In example embodiments where there are multiple devices,such as a laptop computer 120 and ear buds 208, device identificationinformation may be received for each and an evaluation may be made foreach to determine if a match exists. If the device identificationinformation does not match the stored device identification informationfor the user, the NO branch is followed to block 584 of FIG. 5B. Incertain situations, the user 105 may change devices without goingthrough the process of registering the new user device 120 or accessorydevice (e.g., the biometric data device 208). For example, if thebiometric data device (e.g., the ear buds 208) no longer works, the user105 may purchase new ear buds 208. Similarly, the user 105 may changethe type of user device 120 (e.g., a desktop computer, a laptopcomputer, a tablet, a netbook computer, a web-enabled television, avideo game console, a personal digital assistant (PDA), a smart phone,or the like, etc.) that the user is using. It may be beneficial (from aclient services standpoint) to not require the user 105 to go backthrough the device registration process as described in FIG. 4 if theidentity of the user can otherwise be verified. In block 584, an inquiryis conducted to determine if the facial recognition and/or heart ratedata match the user identified by the login information. In one exampleembodiment, the determination may be made by the user verificationmodule 336 and the determination regarding facial recognition matchingand heart rate data matching can be completed as described in otherportions of FIGS. 5A-5B. If the facial recognition and/or heart ratedata did not match for the user identified by the login information, theNO branch is followed to block 580. Otherwise, the YES block is followedto block 586.

In block 586, new user device identification information for the newuser device/biometric data device may be received from the new userdevice/biometric data device as described in FIG. 4. In block 588, thenew user device identification information may be associated with theuser information for the user and stored in a manner substantially thesame as that described in FIG. 4. The process may then continue to block514 of FIG. 5A.

Returning to the inquiry of block 512, if the device identificationinformation does match the stored device information, then the YESbranch can be followed to block 514. At block 514, current user facialimage data is received from the camera 206 of the user device 120. Inone example embodiment, the current user facial image data is receivedby the user verification module 252 of the user device 120. However, insituations where the facial recognition evaluation will take place atthe education server 110, the processor 300 may employ the userverification module 336 to receive the current user facial image datafrom the user device 120 via the network 130.

At block 516, the current user facial image data is compared to thestored face template for the user 105. In example embodiments where thecomparison occurs at the user device 120, the processor 220 may employthe user verification module 252 to receive the face template for theuser from the face template file 246 and can employ the face recognitionmodule 238 to determine if the current user facial image data issufficiently close to the face template to be considered a match. Inexample embodiments where the comparison occurs at the education server110, the processor 300 may employ the user verification module 336 toreceive the face template for the user from the face templates file 324and can employ the facial recognition module 318 to compare anddetermine if the current user facial image data is sufficiently close tothe face template for the user to be considered a match. Those ofordinary skill in the art will recognize that facial recognitionsoftware and algorithms for matching are well known, and, as such, adetailed description of how a match is determined is not necessary.

At block 518, an inquiry is conducted to determine if the current userfacial image data matches the stored face template for the user. Asdiscussed above, in certain example embodiments, the determination canbe made by the facial recognition module 318 or the face recognitionmodule 238. If the current user facial image data does not match thestored face template for the user, the NO branch is followed to block520, where the processor 300 employs the user verification module 336 togenerate a notification. In one example embodiment, the notification canbe that the current user facial image does not match the stored facetemplate for the user. This notification can be sent for display to theuser 105 at the user device 120. In addition, this notification can beassociated with the user data and stored in the user data file 322and/or transmitted to predetermined members of the online educationinstitution for further fraud evaluation. In another example embodiment,the user 105 may be given a predetermined number of opportunities to geta facial recognition match. In that situation, the notification may besent for display to the user and may notify the user of the failure tomatch and request that the user ensure that he/she is properly in frontof the camera 206 and the nothing is obscuring the view of the camera206. The process may then go to either block 514 or block 582.

Returning to the inquiry at block 518, if the current user facial imagedata does match the stored face template, the YES branch is followed toblock 522. In block 522, the processor 300 employs the user verificationmodule 336 to identify the known pattern for the biometric data device208 in use by the user 105, based on, for example, received deviceidentification data for the biometric data device 208. In one exampleembodiment, the biometric data device 208 is ear buds and the knownpattern is as shown in 210. However, other biometric data devices, asdiscussed above, and other known patterns 210 may be substituted. Incertain example embodiments, the user verification module 336 may obtainthe known pattern from the memory 310. Alternatively, in situationswhere the evaluation for the known pattern 210 takes place at the userdevice 120, the processor 220 employs the user verification module 252to identify the known pattern for the biometric data device 208 in useby the user 105. In certain example embodiments, the user verificationmodule 252 may obtain the known pattern from memory 230 or 310.

At block 524, the face recognition module 238 or the facial recognitionmodule 318, using known facial recognition algorithms, may identify fromthe current user facial image data the area(s) where the biometric datadevice 208 is located. In example embodiments where the biometric datadevice 208 is ear buds 208, the particular module would identify the earareas of the user in the current user facial image data for analysis todetermine if the known pattern can be located.

At block 526, the known pattern 210 for the biometric data device 208 iscompared to area(s) of the current user facial image data to determineif the known pattern 210 is identified in the current user facial imagedata. In example embodiments where the evaluation is conducted at theuser device 120, the processor 220 may employ the user verificationmodule 252 and the face recognition module 238 to evaluate the currentuser facial image data to determine if the one or more instances of theknown pattern 210 are present using one or more known facial recognitionalgorithms. For example, if the biometric data device is ear buds 208,the user verification module 252 may determine that two instances of theknown pattern 210 should be viewable (e.g., one on each ear bud next toeach ear of the user 105). Once the comparison is completed, theprocessor 220 may employ the user verification module 252 to generate anotification to the user verification module 336 of the education server110 with the results of the comparison. While the example embodimentpresented describes two instances, the number of instances could befewer or greater. In example embodiments where the evaluation isconducted at the education server 110, the processor 300 may employ theuser verification module 336 and the facial recognition module 318 toevaluate the current user facial image data to determine if the one ormore instances of the known pattern 210 are present using one or moreknown facial recognition algorithms.

At block 528, an inquiry is conducted to determine if one or more of theknown patterns 210 are identified on the biometric data device 208. Ifthe known pattern 210 is not identified in the current user facial imagedata, the NO branch is followed to block 530, where the processor 300employs the user verification module 336 to generate a request fordisplay on the user device 120 that the user put on/uncover thebiometric data device 208 and/or the known pattern 210 on the device208. The process then returns to block 514 to receive an updated currentuser facial image data.

Returning to block 528, if the known pattern 210 is identified in thecurrent user facial image data, the YES branch is followed to block 532,where biometric data for the user is received via the biometric datadevice 208. In one example, the biometric data device is ear buds 208,which contain a heart rate sensor 212 that can receive and convey theheart rate of the user 105 when worn. While the remainder of thediscussion of FIGS. 5A and 5B will describe the biometric data analysiswith regard to heart rate data, other biometric data, as discussedabove, may be substituted in the disclosed method. In one exampleembodiment, the heart rate data is received by the user verificationmodule 252 at the user device 120. In another example embodiment, theheart rate data for the user 105 is transmitted by the user device 120via the network 130 to the education server 110, where the userverification module 336 receives the heart rate data for evaluation.

At block 534, an inquiry is conducted to determine if the received heartrate data for the user is indicative of a live person. In one exampleembodiment where the evaluation is conducted at the user device 120, theuser verification module 252 employs the biosensors module 240 toevaluate the received user heart rate data against known patterns todetermine if the received heart rate data is indicative of a liveperson. Once the evaluation is complete, the processor 220 can employthe user verification module 252 to transmit a notification to the userverification module 336 at the education server 110 via the onlinecourse website indicating the results of the evaluation. Alternatively,in example embodiments where the evaluation is conducted at theeducation server 110, the user verification module 336 employs thebiosensor module 320 to evaluate the received user heart rate dataagainst known heart rate patterns to determine if the received heartrate data is indicative of a live person. If the received heart ratedata is not indicative of a live user, the NO branch is followed toblock 536, where the processor 300 employs the user verification module336 to generate a notification for display on the user device 120 thatthe heart rate data does not indicate a live person and to request thatthe user 105 properly insert the ear buds 208 for heart rate analysis.The process may then return to block 532. In addition, or in thealternative, this notification can be associated with the user data andstored in the user data file 322 and/or transmitted to predeterminedmembers of the online education institution for further fraudevaluation.

Returning to block 534, if the received heart rate data is indicative ofa live user, the YES branch is followed to block 538, where theprocessor 300 employs the user verification module 336 to verify theuser 105 as being authentic. At block 540, the received heart rate datamay be associated with the user information for the user 105 and storedfor subsequent evaluation and comparison. For example, in exampleembodiments where the heart rate data is maintained at the user device120, the processor 220 may employ the user verification module 252 toassociate the heart rate data with the user 105 and to store the heartrate data in the user heart rate data file 250. In example embodimentswhere the heart rate data is maintained at the education server 110, theprocessor 300 may employ the user verification module 336 to associatethe heart rate data with the user 105 and to store the heart rate datain the historical heart rate data file 326.

The processor 300 may then employ the user verification module 336 toprovide or continue providing the user 105 access to the desirededucational information via the user device 120 and the network 130 atblock 542.

At block 544, an inquiry is conducted to determine if a predeterminedamount of time has passed since the verification of the user 105 waschecked. The predetermined amount of time can be anywhere from 1 secondto 120 minutes and can be configurable based on how often the onlineinstitution wants to re-verify and re-authenticate the user 105 or onthe confidence of the system in the probability that the user 105 hasbeen actively working with the course content and has not beensubstituted. In one example embodiment, the determination as to whethera predetermined amount of time has passed can be made by the userverification module 336 of the education server 110. Alternatively,instead of using a predetermined time period to trigger when tore-verify the user, the trigger can be based on random sampling or basedon when the user 105 takes a specific action (e.g., requests to take atest/quiz for a course, requests to answer questions or complete anassignment for a course, etc.). In further alternative embodiments, thetrigger to re-verify the user 105 may be based on a determination by thebiosensor module 320 that the heart rate data received for the user 105is substantially different than the historical heart rate data and maybe an indication that someone else has replaced the user 105 at the userdevice 120. If a predetermined amount of time has not passed, the NObranch is followed back to block 544. On the other hand, if apredetermined amount of time has passed, the YES branch is followed toblock 546.

At block 546, current user facial image data is received from the camera206 of the user device 120. In one example embodiment, the current userfacial image data is received by the user verification module 252 of theuser device 120. However, in situations where the facial recognitionevaluation will take place at the education server 110, the processor300 may employ the user verification module 336 to receive the currentuser facial image data from the user device 120 via the network 130.

At block 548, the current user facial image data is compared to thestored face template for the user 105. In example embodiments where thecomparison occurs at the user device 120, the processor 220 may employthe user verification module 252 to receive the face template for theuser 105 from the face template file 246 and can employ the facerecognition module 238 to determine if the current user facial imagedata is sufficiently close to the face template to be considered amatch. In example embodiments where the comparison occurs at theeducation server 110, the processor 300 may employ the user verificationmodule 336 to receive the face template for the user 105 from the facetemplates file 324 and can employ the facial recognition module 318 tocompare and determine if the current user facial image data issufficiently close to the face template for the user 105 to beconsidered a match.

At block 550, an inquiry is conducted to determine if the current userfacial image data matches the stored face template for the user 105. Asdiscussed above, in certain example embodiments, the determination canbe made by the facial recognition module 318 or the face recognitionmodule 238. If the current user facial image data does not match thestored face template for the user 105, the NO branch is followed toblock 552, where the processor 300 employs the user verification module336 to generate a notification. In one example embodiment, thenotification can be that the current user facial image does not matchthe stored face template for the user 105. This notification can betransmitted for display to the user 105 at the user device 120. Inaddition, this notification can be associated with the user data for theuser 105 and stored in the user data file 322 and/or transmitted topredetermined members of the online education institution for furtherfraud evaluation. In another example embodiment, the user 105 may begiven a certain number of opportunities to get a facial recognitionmatch. In that situation, the notification may be sent for display tothe user 105 and may notify the user 105 of the failure to match andrequest that the user 105 ensure that he/she is properly in front of thecamera 206 and that nothing is obscuring the view of the camera 206. Theprocess may then return to either block 546 or block 582.

Returning to inquiry at block 550, if the current user facial image datadoes match the stored face template, the YES branch is followed to block554. In block 554, the processor 300 employs the user verificationmodule 336 to identify the known pattern for the biometric data device208 in use by the user 105, based on, for example, received deviceidentification data for the biometric data device 208. In one exampleembodiment, the biometric data device 208 is ear buds and the knownpattern is as shown in 210. However, other biometric data devices, asdiscussed above, and other known patterns 210 may be substituted. Incertain example embodiments, the user verification module 336 may obtainthe known pattern from the memory 310. Alternatively, in situationswhere the evaluation for the known pattern 210 takes place at the userdevice 120, the processor 220 employs the user verification module 252to identify the known pattern for the biometric data device 208 in useby the user 105. In certain example embodiments, the user verificationmodule 252 may obtain the known pattern from memory 230 or 310.

At block 556, the face recognition module 238 or the facial recognitionmodule 318, using known facial recognition algorithms, may identify fromthe current user facial image data the area(s) where the biometric datadevice 208 is located. In example embodiments where the biometric datadevice 208 is ear buds 208, the particular module would identify the earareas of the user 105 in the current user facial image data for analysisto determine if the known pattern can be located.

At block 558, the known pattern 210 for the biometric data device 208 iscompared to area(s) of the current user facial image data to determineif the known pattern 210 is identified in the current user facial imagedata. In example embodiments where the evaluation is conducted at theuser device 120, the processor 220 may employ the user verificationmodule 252 and the face recognition module 238 to evaluate the currentuser facial image data to determine if the one or more instances of theknown pattern 210 are present using one or more known facial recognitionalgorithms. For example, if the biometric data device is ear buds 208,the user verification module 252 may determine that two instances of theknown pattern 210 should be viewable (e.g., one on each ear bud next toeach ear of the user 105). Once the comparison is completed, theprocessor 220 may employ the user verification module 252 to generate anotification to the user verification module 336 of the education server110 with the results of the comparison. In example embodiments where theevaluation is conducted at the education server 110, the processor 300may employ the user verification module 336 and the facial recognitionmodule 318 to evaluate the current user facial image data to determineif the one or more instances of the known pattern 210 are present usingone or more known facial recognition algorithms.

At block 560, an inquiry is conducted to determine if one or more of theknown patterns 210 are identified on the biometric data device 208. Ifthe known pattern 210 is not identified in the current user facial imagedata, the NO branch is followed to block 562, where the processor 300employs the user verification module 336 to generate a request fordisplay on the user device 120 that the user 105 put on/uncover thebiometric data device 208 and/or the known pattern 210 on the device208. The process then returns to block 546 to receive an updated currentuser facial image data.

Returning to block 560, if the known pattern 210 is identified in thecurrent user facial image data, the YES branch is followed to block 564,where biometric data, such as heart rate data, for the user 105 isreceived via the biometric data device 208. In one example embodiment,the heart rate data is received by the user verification module 252 atthe user device 120. In another example embodiment, the heart rate datafor the user 105 is transmitted by the user device 120 via the network130 to the education server 110, where the user verification module 336receives the heart rate data for evaluation.

At block 566, an inquiry is conducted to determine if the received heartrate data for the user 105 is indicative of a live person. In oneexample embodiment where the evaluation is conducted at the user device120, the user verification module 252 employs the biosensors module 240to evaluate the received user heart rate data against known patterns todetermine if the received heart rate data is indicative of a liveperson. Once the evaluation is complete, the processor 220 can employthe user verification module 252 to transmit a notification to the userverification module 336 at the education server 110 via the onlinecourse website indicating the results of the evaluation. Alternatively,in example embodiments where the evaluation is conducted at theeducation server 110, the user verification module 336 employs thebiosensor module 320 to evaluate the received user heart rate dataagainst known heart rate patterns to determine if the received heartrate data is indicative of a live person. If the received heart ratedata is not indicative of a live person, the NO branch is followed toblock 568, where the processor 300 employs the user verification module336 to generate a notification for display on the user device 120 thatthe heart rate data does not indicate a live person and to request thatthe user 105 properly insert the ear buds 208 for a heart rate analysis.The process may then return to block 564. In addition, or in thealternative, this notification can be associated with the user data forthe user 105 and stored in the user data file 322 and/or transmitted topredetermined members of the online education institution for furtherfraud evaluation.

Returning to block 566, if the received heart rate data is indicative ofa live user 105, the YES branch is followed to block 570, where thestored heart rate data for the user 105 is retrieved for comparison. Inone example embodiment, the stored heart rate data that is used forcomparison is the most recent heart rate data received for the user 105.In certain example embodiments, the comparison is conducted at the userdevice 120 and the processor employs the user verification module 252 toretrieve the stored heart rate data from the user heart rate data file250. In other example embodiments, the comparison is completed by theeducation server 110 and the processor 300 employs the user verificationmodule 336 to retrieve the stored heart rate data for the user 105 fromthe historical heart rate data file 326.

At block 572, the heart rate data received at block 564 is compared tothe stored heart rate data for the user 105 to determine if the heartrate data matches and/or substantially matches the stored heart ratedata. In example embodiments where the comparison is completed at theuser device 120, the processor 220 can employ the biosensors module 240to compare the heart rate data to the stored heart rate data todetermine if there is a match or substantial match using known matchingalgorithms and can generate a notification to the user verificationmodule 336 of the education server 110 via the network 130 providing theresults of the comparison. In example embodiments where the comparisonis completed at the education server 110, the processor 300 can employthe biosensor module 320 to compare the heart rate data to the storedheart rate data to determine if there is a match or substantial matchusing known matching algorithms. The lack of a match or substantialmatch between the most recent prior heart rate data and the currentheart rate data for the user 105 may indicate that the user 105 haschanged or is attempting to bypass the real-time user verificationsystem by providing artificial data.

At block 574, an inquiry is conducted to determine if the heart ratedata matches or substantially matches the stored heart rate data for theuser 105. If the heart rate data matches or substantially matches thestored heart rate data, the YES branch is followed to block 576, wherethe processor 300 employs the user verification module 336 to verify theuser 105. The process then returns to block 540 of FIG. 5A.

Returning to block 574, if the heart rate data does not match orsubstantially match the stored heart rate data for the user 105, the NObranch is followed to block 578, where the processor 300 employs theuser verification module 336 to generate a notification for display onthe user device 120 that the heart rate data does not match orsubstantially match prior heart rate data for the user 105. In addition,the user 105 may be provided a predetermined number of attempts tocorrect the issue by having further heart rate data compared to storedheart rate data. In addition, or in the alternative, at block 580, thisnotification can be associated with the user data and stored in the userdata file 322 and/or transmitted to predetermined members of the onlineeducation institution for further fraud evaluation. At block 582, theprocessor 300 may employ the user verification module 336 to preventfurther access by the user 105 to the desired online course information.The process may then continue to the END block.

FIG. 6 is a flow chart illustrating an example method 600 for predictiveanalysis of user success in an online education environment, inaccordance with certain example embodiments of the disclosure. Themethod 600 may be performed by the education servers 110 and theprocessors 300 thereon. The example method 600 may be conducted inaddition to or separate from the methods described in FIGS. 4, 5A, and5B. Now referring to FIGS. 1-3 and 6, the exemplary method 600 begins atthe START block and proceeds to block 602, where the processor 300 ofthe education server 110 identifies a class that is being taken by auser 105. For example, the processor 300 could employ the userverification module 336 and determine that the user 105 is about to takea test/quiz in a particular class.

At block 604, the processor 300 may employ the user verification module336 to identify an amount of time that the user 105 has viewed classmaterials for the identified class. For example, the user verificationmodule 336 may evaluate the records for the user 105 in the academicrecords file 330 to determine the amount and/or amount of time the user105 has viewed class materials (e.g., lectures, labs, discussionsessions and boards, etc.) for the class. At block 606, the processor300 may employ the user verification module 336 to identify the user'ssuccess rate (e.g., percentage correct) for practice questions, practicetests, and questions presented during the viewing of course lectures forthe identified class. For example, the user verification module 336 mayevaluate the records for the user 105 in the academic records file 330to determine the user's success rate on practice tests and practicequestions for the class. At block 608, the processor may employ the userverification module 336 to identify the prior scores the user 105received on quizzes for the class. In one example embodiment, the userverification module 336 may evaluate the records for the user 105 in theacademic records file 330 to determine the user's prior quiz scores inthe class.

At block 610, the processor 300 may employ the user verification module336 to build a predictive model of how well the user 105 will do on thecurrent test for the identified class based on the identifiedamount/amount of time the user 105 has viewed class materials, thesuccess rate of the user 105 taking practice questions, practice tests,and questions presented during class lectures or reviews, and priorquizzes taken by the user 105 in the class. Many different known formsof machine learning may be used to build the predictive model based onthe factors outlined above. In certain example embodiments, fewer thanall of the variables or additional variables may be included in buildingthe predictive model. In one example embodiment, the predictive model ofthe user's success on the test may include a score range that the user105 is expected to receive on the current test.

At block 612, the user 105 is provided access to the test via the onlinecourse website. In one example embodiment, the processor 300 may employthe user verification module 336 to monitor in real time the user'ssuccess (e.g., score) on the current test or may only evaluate theuser's success after the user 105 has completed the current test. Atblock 614, the user verification module 336 may receive the current testresults for the user 105. As discussed above, the results may representonly a portion of the current test or the entirety of the current test.

At block 616, an inquiry is conducted to determine if the test result iswithin the predictive model range for the user 105 for the class. In oneexample embodiment, the determination may be made by the userverification module 336 and may be based on a comparison of the testresult to the range of scores provided by the predictive model. If thetest result is within the predictive model range of scores, the YESbranch is followed to the END block. Otherwise, the NO branch isfollowed to block 618. For example, the predictive model may predictbased on the variables provided that the user 105 will receive between70-82 on the test. If the user 105 were to receive a 98 on all or aportion of the test, it may signify the possibility that fraudulentactivity is taking place.

At block 618, the processor 300 may employ the user verification module336 to generate a notification that the user's score on the test is ananomaly (e.g., outside the bounds of the predictive model range ofpotential test scores). At block 620, the notification may betransmitted by the education server 110 to predetermined members of theonline education institution for further fraud evaluation. Thenotification may also be sent for display to the user 105 at the userdevice 120. In addition or in the alternative, this notification can beassociated with the user data and stored in the user data file 322. Themethod 600 may then proceed to the END block.

FIG. 7 is a flow chart illustrating an example method 700 fordetermining when to conduct real-time user verification in an onlineeducation environment, in accordance with certain example embodiments ofthe disclosure. The method 700 may be performed by the education servers110 and the processors 300 thereon. The example method 700 may beconducted in addition to or separate from the methods described in FIGS.4-6. Now referring to FIGS. 1-3 and 7, the exemplary method 700 beginsat the START block and proceeds to block 702, where the processor 300 ofthe education server 110 identifies a class that is being taken by auser 105. For example, the processor 300 could employ the userverification module 336 and determine that the user 105 is about to takea test/quiz or answer a set of questions in a particular class.

At block 704, the processor 300 may employ the user verification module336 to identify an amount and/or amount of time that the user 105 hasviewed class materials for the identified class. For example, the userverification module 336 may evaluate the records for the user 105 in theacademic records file 330 to determine the amount and/or amount of timethe user 105 has viewed class materials (e.g., lectures, labs,discussion sessions and boards, etc.) for the class. At block 706, theprocessor 300 may employ the user verification module 336 to identifythe user's success rate (e.g., percentage correct) for practicequestions, practice tests, and questions presented during the viewing ofcourse lectures for the identified class. For example, the userverification module 336 may evaluate the records for the user 105 in theacademic records file 330 to determine the user's success rate onpractice tests and practice questions for the class. At block 708, theprocessor 300 may employ the user verification module 336 to identifythe prior scores the user 105 received on quizzes for the class. In oneexample embodiment, the user verification module 336 may evaluate therecords for the user 105 in the academic records file 330 to determinethe user's prior quiz scores in the class.

At block 710, the processor 300 may employ the user verification module336 to generate a probability score of how well the user 105 will do onthe current test/quiz/question set for the identified class based on theidentified amount/amount of time the user 105 has viewed classmaterials, the success rate of the user 105 taking practice questions,practice tests, and questions presented during class lectures orreviews, and prior quizzes taken by the user 105 in the class. Manydifferent known forms of machine learning may be used to build theprobability score based on the factors outlined above. In certainexample embodiments, fewer than all of the variables or additionalvariables may be included in building the probability score. In oneexample embodiment, the probability score of the user's success on thetest/quiz/set of questions may include a score range that the user 105is expected to receive on the current test/quiz/set of questions.

At block 712, the user 105 is provided access to the test/quiz/set ofquestions via the online course website. In one example embodiment, theprocessor 300 may employ the user verification module 336 to monitor, inreal time, the user's success (e.g., score) on the current test/quiz/setof questions or may only evaluate the user's success after the user 105has completed the current test/quiz/set of questions. At block 714, theuser verification module 336 may receive the current score results forthe user 105. As discussed above, the results may represent only aportion of the current test/quiz/set of questions or the entirety of thecurrent test/quiz/set of questions.

At block 716, an inquiry is conducted to determine if the actual resultsmeet or exceed the threshold probability score for the user 105 for theparticular assignment for the class. In one example embodiment, thedetermination may be made by the user verification module 336 and may bebased on a comparison of the actual results to the threshold probabilityscore generated. If the actual results are greater than or equal to thethreshold probability score, the NO branch is followed to the END block.Otherwise, the YES branch is followed to block 718. For example, theprobability score may predict, based on the variables provided, that theuser 105 should not be scoring less than an 80 on tests/quizzes and/or aset or questions. If the user 105 were to receive a 75 on all or aportion of the test/quiz/set of questions, it may signify that the user105 should receive additional user verification checks and it should beverified that the user 105 is wearing his/her ear buds 208.

At block 718, an inquiry is conducted to determine if the user 105 iswearing his/her ear buds 208 and/or if the ear buds 208 and/or the user105 are viewable by the camera 206. In certain example embodiments, thedetermination may be made by the user verification module 336. If theuser 105 is not wearing the ear buds 208 or they are not viewable by thecamera 206 or the user 105 is not viewable by the camera 206, the NObranch is followed to block 720. Otherwise, the YES branch is followedto block 722.

At block 720, the processor 300 employs the user verification module 336to generate a request for display on the user device 120 that the user105 put on/uncover the biometric data device 208 and/or the knownpattern 210 on the device 208. At block 722, biometric data, such asheart rate data, for the user 105 is received via the ear buds 208. Inone example embodiment, the heart rate data is received by the userverification module 252 at the user device 120. In another exampleembodiment, the heart rate data for the user 105 is transmitted by theuser device 120 via the network 130 to the education server 110, wherethe user verification module 336 receives the heart rate data forevaluation.

At block 724, an inquiry is conducted to determine if the received heartrate data for the user 105 is indicative of a live person. In oneexample embodiment where the evaluation is conducted at the user device120, the user verification module 252 employs the biosensors module 240to evaluate the received user heart rate data against known patterns todetermine if the received heart rate data is indicative of a liveperson. Once the evaluation is complete, the processor 220 can employthe user verification module 252 to transmit a notification to the userverification module 336 at the education server 110 via the onlinecourse website indicating the results of the evaluation. Alternatively,in example embodiments where the evaluation is conducted at theeducation server 110, the user verification module 336 employs thebiosensor module 320 to evaluate the received user heart rate dataagainst known heart rate patterns to determine if the received heartrate data is indicative of a live person. If the received heart ratedata is not indicative of a live person, the NO branch is followed toblock 726, where the processor 300 employs the user verification module336 to generate a notification for display on the user device 120 thatthe heart rate data does not indicate a live person and to request thatthe user 105 properly insert the ear buds 208 for a heart rate analysis.The process may then return to block 722. In addition, or in thealternative, this notification can be associated with the user data forthe user 105 and stored in the user data file 322 and/or transmitted topredetermined members of the online education institution for furtherfraud evaluation.

Returning to block 724, if the received heart rate data is indicative ofa live user, the YES branch is followed to block 728, where the storedheart rate data for the user 105 is retrieved for comparison. In oneexample embodiment, the stored heart rate data that is used forcomparison is the most recent heart rate data received for the user 105.In certain example embodiments, the comparison is conducted at the userdevice 120 and the processor 220 employs the user verification module252 to retrieve the stored heart rate data from the user heart rate datafile 250. In other example embodiments, the comparison is completed bythe education server 110 and the processor 300 employs the userverification module 336 to retrieve the stored heart rate data for theuser 105 from the historical heart rate data file 326.

At block 730, the heart rate data received at block 728 is compared tothe stored heart rate data for the user 105 to determine if the heartrate data matches and/or substantially matches the stored heart ratedata. In example embodiments where the comparison is completed at theuser device 120, the processor 220 can employ the biosensors module 240to compare the heart rate data to the stored heart rate data todetermine if there is a match or substantial match using known matchingalgorithms and can generate a notification to the user verificationmodule 336 of the education server 110 via the network 130 providing theresults of the comparison. In example embodiments where the comparisonis completed at the education server 110, the processor 300 can employthe biosensor module 320 to compare the heart rate data to the storedheart rate data to determine if there is a match or substantial matchusing known matching algorithms. The lack of a match or substantialmatch between the most recent prior heart rate data and the currentheart rate data for the user 105 may indicate that the user 105 haschanged or is attempting to bypass the real-time user verificationsystem by providing artificial data.

At block 732, an inquiry is conducted to determine if the heart ratedata matches or substantially matches the stored heart rate data for theuser 105. If the heart rate data matches or substantially matches thestored heart rate data, the YES branch is followed to block 734, wherethe processor 300 employs the user verification module 336 to verify theuser 105 is authentic. The process then continues to the END block.

Returning to block 732, if the heart rate data does not match orsubstantially match the stored heart rate data for the user 105, the NObranch is followed to block 736, where the processor 300 employs theuser verification module 336 to generate a notification for display onthe user device 120 that the heart rate data does not match orsubstantially match prior heart rate data for the user 105. In addition,the user 105 may be provided a predetermined number of attempts tocorrect the issue by having further heart rate data compared to storedheart rate data. In addition, or in the alternative, this notificationcan be associated with the user data and stored in the user data file322 and/or transmitted to predetermined members of the online educationinstitution for further fraud evaluation.

Embodiments described herein may be implemented using hardware,software, and/or firmware, for example, to perform the methods and/oroperations described herein. Certain embodiments described herein may beprovided as one or more tangible machine-readable media storingmachine-executable instructions that, if executed by a machine, causethe machine to perform the methods and/or operations described herein.The tangible machine-readable media may include, but is not limited to,any type of disk including floppy disks, optical disks, compact diskread-only memories (CD-ROMs), compact disk rewritables (CD-RWs), andmagneto-optical disks, semiconductor devices such as read-only memories(ROMs), random access memories (RAMs) such as dynamic and static RAMs,erasable programmable read-only memories (EPROMs), electrically erasableprogrammable read-only memories (EEPROMs), flash memories, magnetic oroptical cards, or any type of tangible media suitable for storingelectronic instructions. The machine may include any suitable processingor computing platform, device or system and may be implemented using anysuitable combination of hardware and/or software. The instructions mayinclude any suitable type of code and may be implemented using anysuitable programming language. In other embodiments, machine-executableinstructions for performing the methods and/or operations describedherein may be embodied in firmware. Additionally, in certainembodiments, a special-purpose computer or a particular machine may beformed in order to identify actuated input elements and process theidentifications.

Various features, aspects, and embodiments have been described herein.The features, aspects, and embodiments are susceptible to combinationwith one another as well as to variation and modification, as will beunderstood by those having skill in the art. The present disclosureshould, therefore, be considered to encompass such combinations,variations, and modifications.

The terms and expressions which have been employed herein are used asterms of description and not of limitation, and there is no intention,in the use of such terms and expressions, of excluding any equivalentsof the features shown and described (or portions thereof), and it isrecognized that various modifications are possible within the scope ofthe claims. Other modifications, variations, and alternatives are alsopossible. Accordingly, the claims are intended to cover all suchequivalents.

While certain embodiments of the invention have been described inconnection with what is presently considered to be the most practicaland various embodiments, it is to be understood that the invention isnot to be limited to the disclosed embodiments, but on the contrary, isintended to cover various modifications and equivalent arrangementsincluded within the scope of the claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense only,and not for purposes of limitation.

This written description uses examples to disclose certain exampleembodiments, including the best mode, and also to enable any personskilled in the art to practice certain embodiments of the invention,including making and using any devices or systems and performing anyincorporated methods. The patentable scope of certain embodiments of theinvention is defined in the claims, and may include other examples thatoccur to those skilled in the art. Such other examples are intended tobe within the scope of the claims if they have structural elements thatdo not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

Example embodiments of the disclosure may include a computer-implementedmethod that may include: receiving, by an education server comprisingone or more processors from a user device, user identifying informationassociated with a user at the user device; receiving, by the educationserver from the user device, a request to access online educationcontent; determining, by the education server and based at least in parton the user identifying information, a face template based on historicalfacial image data for the user; comparing, by the education server, thecurrent facial image data from the user device to the historical facialimage data for the user to determine if the current facial image datamatches the face template for the user; receiving, by the educationserver, a biometric sensor data for the user; determining, by theeducation server and based at least in part on the biometric sensordata, if the user is located at the user device; and verifying, by theeducation server, the user to access the online education content,wherein verifying comprises facilitating, by the education server andbased at least in part on the determination that the current facialimage data matches the face template for the user and the biometricsensor data indicates the user is located at the user device, access tothe online education content by the user device.

Further example embodiments may include the computer-implemented methodthat may include: determining, by the education server and based atleast in part on the user identifying information, a stored device IDfor a first device associated with the user; and comparing, by theeducation server, a current device ID for the user device to the storeddevice ID to determine if the current device ID matches the storeddevice ID; wherein facilitating access to the online education contentis further based at least in part on the determination that the currentdevice ID matches the stored device ID.

Further still, example embodiments of the disclosure may include acomputer-implemented method that may include: determining, by theeducation server and based at least in part on the user identifyinginformation, a biometric data device associated with the user;determining, by the education server, a known pattern on an exterior ofthe biometric data device; and evaluating, by the education server, thecurrent facial image data to determine if the current facial image datacomprises the known pattern on the exterior of the biometric datadevice; wherein facilitating access to the online education content isfurther based at least in part on the determination that the currentfacial image data comprises the known pattern on the exterior of thebiometric data device. Further still, the biometric data device maycomprise ear buds, wherein the ear buds comprise a heart rate monitorfor receiving heart rate data of the user, and wherein the biometricsensor data comprises the heart rate data for the user.

Further example embodiments may include the biometric sensor datacomprising heart rate data of the user, wherein the computer-implementedmethod may also include: determining, by the education server, that apredetermined amount of time has passed since the user was verified toaccess the online education content; receiving, by the education server,a current heart rate data for the user; accessing, by the educationserver, historical heart rate data for the user; comparing, by theeducation server, the current heart rate data for the user to thehistorical heart rate data for the user to determine if the historicalheart rate data and the current heart rate data are from a same user;and facilitating, by the education server and based at least in part onthe determination that the historical heart rate data and the currentheart rate data are from the same user, access to the online educationcontent by the user device. In addition, example embodiments may includethe computer-implemented method that may include: identifying, by theeducation server, an online education course enrolled in by the user;determining, by the education server, a test to be provided to the userfor the online education course; identifying, by the education server, aplurality of historical education data for the user in the onlineeducation course; generating, by the education server and based at leastin part on the plurality of historical education data, a predictivemodel of success for the user on the test, wherein the predictive modelcomprises a test score range comprising a maximum test score threshold;receiving, by the education server, a test result for the user on thetest; comparing, by the education server, the test result for the userto the maximum test score threshold; and generating, by the educationserver and based on the determination that the test result is greaterthan the maximum test score threshold, a notification that the testresult violates the maximum test core threshold for the user in theonline education course.

Further example embodiments may include the computer-implemented methodthat may include: identifying, by the education server, an onlineeducation course enrolled in by the user; identifying, by the educationserver, a plurality of historical education data for the user in theonline education course; generating, by the education server and basedat least in part on the plurality of historical education data, aprobability score for the user on an online course assignment, whereinthe probability score comprises a minimum score threshold; receiving, bythe education server, a score for the user on the online courseassignment; comparing, by the education server, the score for the useron the online course assignment to the minimum score threshold;identifying, by the education server and based on the determination thatthe score is less than the minimum score threshold, if the user iswearing a biometric data device; and generating, by the education serverand based at least in part on the identification that the user is notwearing the biometric data device and the score is less than the minimumscore threshold, a notification to the user to put on the biometric datadevice. Further, example embodiments may include: preventing, by theeducation server, user access to the online education content based atleast in part on the determination that the current facial image datadoes not match the face template for the user or the biometric sensordata does not indicate the user is located at the user device.

Still further example embodiments of the disclosure may include: anon-transitory computer-readable media comprising computer-executableinstructions that, when executed by one or more processors, configurethe one or more processors to perform operations comprising: receiving,from a user device, user identifying information associated with a userat the user device; receiving, from the user device, a request to accessonline education content; determining, based at least in part on theuser identifying information, a face template based on historical facialimage data for the user; comparing the current facial image data fromthe user device to the historical facial image data for the user todetermine if the current facial image data matches the face template forthe user; receiving a biometric sensor data for the user; determining,based at least in part on the biometric sensor data, if the user islocated at the user device; and verifying the user to access the onlineeducation content, wherein verifying comprises facilitating, based atleast in part on the determination that the current facial image datamatches the face template for the user and the biometric sensor dataindicates the user is located at the user device, access to the onlineeducation content by the user device.

Yet further example embodiments may include the non-transitorycomputer-readable media, wherein the operations may further include:determining, based at least in part on the user identifying information,a stored device ID for a first device associated with the user; andcomparing a current device ID for the user device to the stored deviceID to determine if the current device ID matches the stored device ID;wherein facilitating access to the online education content is furtherbased at least in part on the determination that the current device IDmatches the stored device ID. Still further example embodiments mayinclude the non-transitory computer-readable media, wherein theoperations may further include: determining, based at least in part onthe user identifying information, a biometric data device associatedwith the user; determining a known pattern on an exterior of thebiometric data device; and evaluating, the current facial image data todetermine if the current facial image data comprises the known patternon the exterior of the biometric data device; wherein facilitatingaccess to the online education content is further based at least in parton the determination that the current facial image data comprises theknown pattern on the exterior of the biometric data device. Further,example embodiments may include the non-transitory computer-readablemedia, wherein the biometric data device comprises ear buds, wherein theear buds comprise a heart rate monitor for receiving heart rate data ofthe user, and wherein the biometric sensor data comprises the heart ratedata for the user.

In addition, example embodiments may include the biometric sensor datacomprising heart rate data of the user, wherein the operations of thenon-transitory computer-readable media may further include: determiningthat a predetermined amount of time has passed since the user wasverified to access the online education content; receiving a currentheart rate data for the user; accessing historical heart rate data forthe user; comparing the current heart rate data for the user to thehistorical heart rate data for the user to determine if the historicalheart rate data and the current heart rate data are from a same user;and facilitating, based at least in part on the determination that thehistorical heart rate data and the current heart rate data are from thesame user, access to the online education content by the user device.Yet further, example embodiments may include the non-transitorycomputer-readable media, wherein the operations may further include:identifying an online education course enrolled in by the user;determining a test to be provided to the user for the online educationcourse; identifying a plurality of historical education data for theuser in the online education course; generating, based at least in parton the plurality of historical education data, a predictive model ofsuccess for the user on the test, wherein the predictive model comprisesa test score range comprising a maximum test score threshold; receivinga test result for the user on the test; comparing the test result forthe user to the maximum test score threshold; and generating, based onthe determination that the test result is greater than the maximum testscore threshold, a notification that the test result violates themaximum test core threshold for the user in the online education course.

Still further, example embodiments may include the non-transitorycomputer-readable media, wherein the operations may further include:identifying an online education course enrolled in by the user;identifying a plurality of historical education data for the user in theonline education course; generating, and based at least in part on theplurality of historical education data, a probability score for the useron an online course assignment, wherein the probability score comprisesa minimum score threshold; receiving a score for the user on the onlinecourse assignment; comparing the score for the user on the online courseassignment to the minimum score threshold; identifying, based on thedetermination that the score is less than the minimum score threshold,if the user is wearing a biometric data device; and generating, based atleast in part on the identification that the user is not wearing thebiometric data device and the score is less than the minimum scorethreshold, a notification to the user to put on the biometric datadevice. In addition, example embodiments may include the non-transitorycomputer-readable media, wherein the operations may further include:preventing user access to the online education content based at least inpart on the determination that the current facial image data does notmatch the face template for the user or the biometric sensor data doesnot indicate the user is located at the user device.

Further example embodiments of the disclosure may include a system,comprising: at least one memory that stores computer-executableinstructions and at least one processor configured to access the atleast one memory, wherein the at least one processor is configured toexecute the computer-executable instructions to: receive, from a userdevice, user identifying information associated with a user at the userdevice; receive, from the user device, a request to access onlineeducation content; determine, based at least in part on the useridentifying information, a face template based on historical facialimage data for the user; compare the current facial image data from theuser device to the historical facial image data for the user todetermine if the current facial image data matches the face template forthe user; receive a biometric sensor data for the user; determine, basedat least in part on the biometric sensor data, if the user is located atthe user device; and verify the user to access the online educationcontent, wherein verifying comprises facilitating, based at least inpart on the determination that the current facial image data matches theface template for the user and the biometric sensor data indicates theuser is located at the user device, access to the online educationcontent by the user device.

Example embodiments of the system may further include: the at least oneprocessor being further configured to execute the computer-executableinstructions to determine, based at least in part on the useridentifying information, a stored device ID for a first deviceassociated with the user; and compare a current device ID for the userdevice to the stored device ID to determine if the current device IDmatches the stored device ID; wherein facilitating access to the onlineeducation content is further based at least in part on the determinationthat the current device ID matches the stored device ID. Exampleembodiments of the system may further include: the at least oneprocessor being further configured to execute the computer-executableinstructions to determine, based at least in part on the useridentifying information, a biometric data device associated with theuser; determine a known pattern on an exterior of the biometric datadevice; and evaluate the current facial image data to determine if thecurrent facial image data comprises the known pattern on the exterior ofthe biometric data device; wherein facilitating access to the onlineeducation content is further based at least in part on the determinationthat the current facial image data comprises the known pattern on theexterior of the biometric data device. In further example embodiments ofthe system, the biometric data device comprises ear buds, wherein theear buds comprise a heart rate monitor for receiving heart rate data ofthe user, and wherein the biometric sensor data comprises the heart ratedata for the user.

Example embodiments of the system may further include the biometricsensor data comprising heart rate data of the user, wherein the at leastone processor is further configured to execute the computer-executableinstructions to: determine that a predetermined amount of time haspassed since the user was verified to access the online educationcontent; receive a current heart rate data for the user; accesshistorical heart rate data for the user; compare the current heart ratedata for the user to the historical heart rate data for the user todetermine if the historical heart rate data and the current heart ratedata are from a same user; and facilitate, based at least in part on thedetermination that the historical heart rate data and the current heartrate data are from the same user, access to the online education contentby the user device. Example embodiments of the system may furtherinclude: the at least one processor being further configured to executethe computer-executable instructions to identify an online educationcourse enrolled in by the user; determine a test to be provided to theuser for the online education course; identify a plurality of historicaleducation data for the user in the online education course; generate,based at least in part on the plurality of historical education data, apredictive model of success for the user on the test, wherein thepredictive model comprises a test score range comprising a maximum testscore threshold; receive a test result for the user on the test; comparethe test result for the user to the maximum test score threshold; andgenerate, based on the determination that the test result is greaterthan the maximum test score threshold, a notification that the testresult violates the maximum test core threshold for the user in theonline education course.

Example embodiments of the system may further include: the at least oneprocessor being further configured to execute the computer-executableinstructions to identify an online education course enrolled in by theuser; identify a plurality of historical education data for the user inthe online education course; generate, and based at least in part on theplurality of historical education data, a probability score for the useron an online course assignment, wherein the probability score comprisesa minimum score threshold; receive a score for the user on the onlinecourse assignment; compare the score for the user on the online courseassignment to the minimum score threshold; identify, based on thedetermination that the score is less than the minimum score threshold,if the user is wearing a biometric data device; and generate, based atleast in part on the identification that the user is not wearing thebiometric data device and the score is less than the minimum scorethreshold, a notification to the user to put on the biometric datadevice. Example embodiments of the system may further include: the atleast one processor being further configured to execute thecomputer-executable instructions to prevent user access to the onlineeducation content based at least in part on the determination that thecurrent facial image data does not match the face template for the useror the biometric sensor data does not indicate the user is located atthe user device.

Further example embodiments of the disclosure may include an apparatuscomprising: at least one memory storing computer-executableinstructions; and at least one processor, wherein the at least oneprocessor is configured to access the at least one memory and to executethe computer-executable instructions to: receive, from a user device,user identifying information associated with a user at the user device;receive, from the user device, a request to access online educationcontent; determine, based at least in part on the user identifyinginformation, a face template based on the historical facial image datafor the user; compare the current facial image data from the user deviceto the historical facial image data for the user to determine if thecurrent facial image data matches the face template for the user;receive a biometric sensor data for the user; determine, based at leastin part on the biometric sensor data, if the user is located at the userdevice; and verify the user to access the online education content,wherein verification comprises facilitating, based at least in part onthe determination that the current facial image data matches the facetemplate for the user and the biometric sensor data indicates the useris located at the user device, access to the online education content bythe user device.

Example embodiments of the apparatus may further include: the at leastone processor being further configured to execute thecomputer-executable instructions to: determine, based at least in parton the user identifying information, a stored device ID for a firstdevice associated with the user; and compare a current device ID for theuser device to the stored device ID to determine if the current deviceID matches the stored device ID; wherein facilitating access to theonline education content is further based at least in part on thedetermination that the current device ID matches the stored device ID.In addition, example embodiments of the apparatus may also include: theat least one processor being further configured to execute thecomputer-executable instructions to: determine, based at least in parton the user identifying information, a biometric data device associatedwith the user; determine a known pattern on an exterior of the biometricdata device; and evaluate the current facial image data to determine ifthe current facial image data comprises the known pattern on theexterior of the biometric data device; wherein facilitating access tothe online education content is further based at least in part on thedetermination that the current facial image data comprises the knownpattern on the exterior of the biometric data device. Further, exampleembodiments of the apparatus may include the biometric data devicecomprising ear buds, wherein the ear buds comprise a heart rate monitorfor receiving heart rate data of the user, and wherein the biometricsensor data comprises the heart rate data for the user.

Still further, example embodiments of the apparatus may include: the atleast one processor being further configured to execute thecomputer-executable instructions to: determine that a predeterminedamount of time has passed since the user was verified to access theonline education content; receive a current heart rate data for theuser; access historical heart rate data for the user; compare thecurrent heart rate data for the user to the historical heart rate datafor the user to determine if the historical heart rate data and thecurrent heart rate data are from a same user; and facilitate, based atleast in part on the determination that the historical heart rate dataand the current heart rate data are from the same user, access to theonline education content by the user device. Example embodiments of theapparatus may also include: the at least one processor being furtherconfigured to execute the computer-executable instructions to: identifyan online education course enrolled in by the user; determine a test tobe provided to the user for the online education course; identify aplurality of historical education data for the user in the onlineeducation course; generate based at least in part on the plurality ofhistorical education data, a predictive model of success for the user onthe test, wherein the predictive model comprises a test score rangecomprising a maximum test score threshold; receive a test result for theuser on the test; compare the test result for the user to the maximumtest score threshold; and generate, based on the determination that thetest result is greater than the maximum test score threshold, anotification that the test result violates the maximum test corethreshold for the user in the online education course.

Still further, example embodiments of the apparatus may also include:the at least one processor being further configured to execute thecomputer-executable instructions to: identify an online education courseenrolled in by the user; identify a plurality of historical educationdata for the user in the online education course; generate, and based atleast in part on the plurality of historical education data, aprobability score for the user on an online course assignment, whereinthe probability score comprises a minimum score threshold; receive ascore for the user on the online course assignment; compare the scorefor the user on the online course assignment to the minimum scorethreshold; identify, based on the determination that the score is lessthan the minimum score threshold, if the user is wearing a biometricdata device; and generate, based at least in part on the identificationthat the user is not wearing the biometric data device and the score isless than the minimum score threshold, a notification to the user to puton the biometric data device. In addition, example embodiments of theapparatus may include: the at least one processor being furtherconfigured to execute the computer-executable instructions to preventuser access to the online education content based at least in part onthe determination that the current facial image data does not match theface template for the user or the biometric sensor data does notindicate the user is located at the user device.

Additional example embodiments of the disclosure may include: a systemcomprising: a means for receiving, from a user device, user identifyinginformation associated with a user at the user device; a means forreceiving, from the user device, a request to access online educationcontent; a means for determining, based at least in part on the useridentifying information, a face template based on the historical facialimage data for the user; a means for comparing the current facial imagedata from the user device to the historical facial image data for theuser to determine if the current facial image data matches the facetemplate for the user; a means for receiving a biometric sensor data forthe user; a means for determining, based at least in part on thebiometric sensor data, if the user is located at the user device; and ameans for verifying the user for access to the online education content,wherein verifying comprises a means for facilitating, based at least inpart on the determination that the current facial image data matches theface template for the user and the biometric sensor data indicates theuser is located at the user device, access to the online educationcontent by the user device.

In addition, example embodiments of the system may include: a means fordetermining, based at least in part on the user identifying information,a stored device ID for a first device associated with the user; and ameans for comparing a current device ID for the user device to thestored device ID to determine if the current device ID matches thestored device ID; wherein facilitating access to the online educationcontent is further based at least in part on the determination that thecurrent device ID matches the stored device ID. Further, exampleembodiments of the system may also include: a means for determining,based at least in part on the user identifying information, a biometricdata device associated with the user; a means for determining a knownpattern on an exterior of the biometric data device; and a means forevaluating the current facial image data to determine if the currentfacial image data comprises the known pattern on the exterior of thebiometric data device; wherein facilitating access to the onlineeducation content is further based at least in part on the determinationthat the current facial image data comprises the known pattern on theexterior of the biometric data device. Still further, exampleembodiments of the system may include: the biometric data devicecomprising ear buds, wherein the ear buds comprise a heart rate monitorfor receiving the heart rate data of the user, and wherein the biometricsensor data comprises the heart rate data for the user.

The example embodiments of the system may also include: the biometricsensor data comprising heart rate data of the user, wherein the systemfurther comprises: means for determining that a predetermined amount oftime has passed since the user was verified to access the onlineeducation content; means for receiving a current heart rate data for theuser; means for accessing historical heart rate data for the user; meansfor comparing the current heart rate data for the user to the historicalheart rate data for the user to determine if the historical heart ratedata and the current heart rate data are from a same user; and means forfacilitating, based at least in part on the determination that thehistorical heart rate data and the current heart rate data are from thesame user, access to the online education content by the user device. Inaddition, example embodiments of the system may include: means foridentifying an online education course enrolled in by the user; meansfor determining a test to be provided to the user for the onlineeducation course; means for identifying a plurality of historicaleducation data for the user in the online education course; means forgenerating, based at least in part on the plurality of the historicaleducation data, a predictive model of success for the user on the test,wherein the predictive model comprises a test score range comprising amaximum test score threshold; means for receiving a test result for theuser on the test; means for comparing the test result for the user tothe maximum test score threshold; and means for generating, based on thedetermination that the test result is greater than the maximum testscore threshold, a notification that the test result violates themaximum test core threshold for the user in the online education course.

Furthermore, example embodiments of the system may also include: meansfor identifying an online education course enrolled in by the user;means for identifying a plurality of historical education data for theuser in the online education course; means for generating, based atleast in part on the plurality of historical education data, aprobability score for the user on an online course assignment, whereinthe probability score comprises a minimum score threshold; means forreceiving a score for the user on the online course assignment; meansfor comparing the score for the user on the online course assignment tothe minimum score threshold; means for identifying, based on thedetermination that the score is less than the minimum score threshold,if the user is wearing a biometric data device; and means forgenerating, based at least in part on the identification that the useris not wearing the biometric data device and the score is less than theminimum score threshold, a notification to the user to put on thebiometric data device. In addition, example embodiments of the systemmay include: means for preventing user access to the online educationcontent based at least in part on the determination that the currentfacial image data does not match the face template for the user or thebiometric sensor data does not indicate the user is located at the userdevice.

The claimed invention is:
 1. A non-transitory computer-readable mediumcomprising computer-executable instructions that, when executed by oneor more processors, configure the one or more processors to performoperations comprising: receiving, from a user device, a request toaccess online education content; determining a face template based onhistorical facial image data for a user; receiving, from the userdevice, a current facial image data for the user; receiving, from theuser device, user identifying information associated with the user atthe user device; determining, based at least in part on the useridentifying information, a biometric data device associated with theuser, wherein the biometric data device is configured to receive abiometric sensor data for the user; determining a known pattern on anexterior of the biometric data device; evaluating, the current facialimage data to determine if the current facial image data comprises theknown pattern on the exterior of the biometric data device; comparingthe current facial image data to the historical facial image data forthe user to determine if the current facial image data matches the facetemplate for the user; receiving, at the biometric data device, thebiometric sensor data for the user; determining, based on the biometricsensor data, if the user is located at the user device; and verifyingthe user to access the online education content, wherein verificationcomprises facilitating, based at least in part on the determination thatthe current facial image data matches the face template for the user andthe biometric sensor data indicates the user is located at the userdevice, access to the online education content by the user device,wherein facilitating access to the online education content is furtherbased at least in part on the determination that the current facialimage data comprises the known pattern on the exterior of the biometricdata device.
 2. The non-transitory computer-readable medium of claim 1,wherein the operations further comprise: receiving, from the userdevice, user identifying information associated with the user at theuser device; determining, based at least in part on the user identifyinginformation, a stored device ID for a first device associated with theuser; and comparing a current device ID for the user device to thestored device ID to determine if the current device ID matches thestored device ID; wherein facilitating access to the online educationcontent is further based at least in part on the determination that thecurrent device ID matches the stored device ID.
 3. The non-transitorycomputer-readable medium of claim 1, wherein the biometric sensor datacomprises heart rate data of the user.
 4. The non-transitorycomputer-readable medium of claim 3, wherein the operations furthercomprise: determining that a predetermined amount of time has passedsince the user was verified to access the online education content;receiving a current heart rate data for the user; accessing historicalheart rate data for the user; comparing the current heart rate data forthe user to the historical heart rate data for the user to determine ifthe historical heart rate data and the current heart rate data are froma same user; and facilitating, based at least in part on thedetermination that the historical heart rate data and the current heartrate data are from the same user, access to the online education contentby the user device.
 5. The non-transitory computer-readable medium ofclaim 1, wherein the operations further comprise: identifying an onlineeducation course enrolled in by the user; determining a test to beprovided to the user for the online education course; identifying aplurality of historical education data for the user in the onlineeducation course; generating, based at least in part on the plurality ofhistorical education data, a predictive model of success for the user onthe test, wherein the predictive model comprises a test score rangecomprising a maximum test score threshold; receiving a test result forthe user on the test; comparing the test result for the user to themaximum test score threshold; and generating, based on the determinationthat the test result is greater than the maximum test score threshold, anotification that the test result violates the maximum test scorethreshold for the user in the online education course.
 6. Thenon-transitory computer-readable medium of claim 1, wherein theoperations further comprise: receiving a score for the user on an onlinecourse assignment; comparing the score for the user on the online courseassignment to a minimum score threshold; identifying, based on thedetermination that the score is less than the minimum score threshold,if the user is wearing a biometric data device; and generating, based atleast in part on the identification that the user is not wearing thebiometric data device and the score is less than the minimum scorethreshold, a notification to the user to put on the biometric datadevice.
 7. The non-transitory computer-readable medium of claim 6,wherein the operations further comprise: identifying an online educationcourse enrolled in by the user; identifying a plurality of historicaleducation data for the user in the online education course; andgenerating, based at least in part on the plurality of historicaleducation data, a probability score for the user on an online courseassignment, wherein the probability score comprises the minimum scorethreshold.
 8. The non-transitory computer-readable medium of claim 1,wherein the operations further comprise: preventing user access to theonline education content based at least in part on the determinationthat current facial image data does not match the face template for theuser or the biometric sensor data does not indicate the user is locatedat the user device.
 9. A computer-implemented method, comprising:receiving, by an education server comprising one or more processors froma user device, a request to access online education content;determining, by the education server, a face template based onhistorical facial image data for a user; receiving, by the educationserver from the user device, a current facial image data for the user;receiving, by the education server from the user device, useridentifying information associated with the user at the user device;determining, by the education server and based at least in part on theuser identifying information, a biometric data device associated withthe user, wherein the biometric data device comprises ear budscomprising a heart rate monitor for receiving a biometric sensor data ofthe user; determining, by the education server, a known pattern on anexterior of the ear buds; evaluating, by the education server, thecurrent facial image data to determine if the current facial image datacomprises the known pattern on the exterior of the ear buds; comparing,by the education server, the current facial image data to the historicalfacial image data for the user to determine if the current facial imagedata matches the face template for the user; receiving, by the educationserver, the biometric sensor data for the user; determining, by theeducation server and based on the biometric sensor data, if the user islocated at the user device; and verifying, by the education server, theuser for access to the online education content, wherein verifyingcomprises facilitating, by the education server and based at least inpart on the determination that the current facial image data matches theface template for the user and the biometric sensor data indicates theuser is located at the user device, access to the online educationcontent by the user device, wherein facilitating access to the onlineeducation content is further based at least in part on the determinationthat the current facial image data comprises the known pattern on theexterior of the ear buds.
 10. The computer-implemented method of claim9, wherein the biometric sensor data comprises heart rate data of theuser and wherein the method further comprises: determining, by theeducation server, that a predetermined amount of time has passed sincethe user was verified to access the online education content; receiving,by the education server, a current heart rate data for the user;accessing, by the education server, historical heart rate data for theuser; comparing, by the education server, the current heart rate datafor the user to the historical heart rate data for the user to determineif the historical heart rate data and the current heart rate data arefrom a same user; and facilitating, by the education server and based atleast in part on the determination that the historical heart rate dataand the current heart rate data are from the same user, access to theonline education content by the user device.
 11. Thecomputer-implemented method of claim 9, further comprising: identifying,by the education server, an online education course enrolled in by theuser; identifying, by the education server, a plurality of historicaleducation data for the user in the online education course; generating,by the education server and based at least in part on the plurality ofhistorical education data, a probability score for the user on an onlinecourse assignment, wherein the probability score comprises a minimumscore threshold; receiving, by the education server, a score for theuser on an online course assignment; comparing, by the education server,the score for the user on the online course assignment to the minimumscore threshold; identifying, by the education server and based on thedetermination that the score is less than the minimum score threshold,if the user is wearing a biometric data device; and generating, by theeducation server and based at least in part on the identification thatthe user is not wearing the biometric data device and the score is lessthan the minimum score threshold, a notification to the user to put onthe biometric data device.
 12. A system, comprising: at least one memorythat stores computer-executable instructions; at least one processorconfigured to access the at least one memory, wherein the at least oneprocessor is configured to execute the computer-executable instructionsto: receive, from a user device, a request to access online educationcontent; determine a face template based on historical facial image datafor a user; receive, by an education server from the user device, acurrent facial image data for the user; receive, from the user device,user identifying information associated with the user at the userdevice; determine, based at least in part on the user identifyinginformation, a biometric data device associated with the user, whereinthe biometric data device is configured to receive a biometric sensordata for the user; determine a known pattern on an exterior of thebiometric data device; evaluate, the current facial image data todetermine if the current facial image data comprises the known patternon the exterior of the biometric data device; compare the current facialimage data to the historical facial image data for the user to determineif the current facial image data matches the face template for the user;receive the biometric sensor data for the user; determine, based on thebiometric sensor data, if the user is located at the user device; andverify the user to access the online education content, whereinverification comprises facilitating, based at least in part on thedetermination that the current facial image data matches the facetemplate for the user and the biometric sensor data indicates the useris located at the user device, access to the online education content bythe user device, wherein facilitating access to the online educationcontent is further based at least in part on the determination that thecurrent facial image data comprises the known pattern on the exterior ofthe biometric data device.
 13. The system of claim 12, wherein the atleast one processor is further configured to execute thecomputer-executable instructions to: receive, from the user device, useridentifying information associated with the user at the user device;determine, based at least in part on the user identifying information, astored device ID for a first device associated with the user; andcompare a current device ID for the user device to the stored device IDto determine if the current device ID matches the stored device ID;wherein facilitating access to the online education content is furtherbased at least in part on the determination that the current device IDmatches the stored device ID.
 14. The system of claim 12, wherein thebiometric sensor data comprises heart rate data of the user and whereinthe at least one processor is further configured to execute thecomputer-executable instructions to: determine that a predeterminedamount of time has passed since the user was verified to access theonline education content; receive a current heart rate data for theuser; access historical heart rate data for the user; compare thecurrent heart rate data for the user to the historical heart rate datafor the user to determine if the historical heart rate data and thecurrent heart rate data are from a same user; and facilitate, based atleast in part on the determination that the historical heart rate dataand the current heart rate data are from the same user, access to theonline education content by the user device.
 15. The system of claim 12,wherein the at least one processor is further configured to execute thecomputer-executable instructions to: identify an online education courseenrolled in by the user; determine a test to be provided to the user forthe online education course; identify a plurality of historicaleducation data for the user in the online education course; generatebased at least in part on the plurality of historical education data, apredictive model of success for the user on the test, wherein thepredictive model comprises a test score range comprising a maximum testscore threshold; receive a test result for the user on the test; comparethe test result for the user to the maximum test score threshold; andgenerate, based on the determination that the test result is greaterthan the maximum test score threshold, a notification that the testresult violates the maximum test score threshold for the user in theonline education course.
 16. The system of claim 12, wherein the atleast one processor is further configured to execute thecomputer-executable instructions to: identify an online education courseenrolled in by the user; identify a plurality of historical educationdata for the user in the online education course; generate, based atleast in part on the plurality of historical education data, aprobability score for the user on an online course assignment, whereinthe probability score comprises a minimum score threshold; receive ascore for the user on an online course assignment; compare the score forthe user on the online course assignment to the minimum score threshold;identify, based on the determination that the score is less than theminimum score threshold, if the user is wearing a biometric data device;and generate, based at least in part on the identification that the useris not wearing the biometric data device and the score is less than theminimum score threshold, a notification to the user to put on thebiometric data device.
 17. The system of claim 12, wherein the at leastone processor is further configured to execute the computer-executableinstructions to prevent user access to the online education contentbased at least in part on the determination that current facial imagedata does not match the face template for the user or the biometricsensor data does not indicate the user is located at the user device.18. The non-transitory computer-readable medium of claim 1, wherein thebiometric data device comprises ear buds, wherein the ear buds comprisea heart rate monitor for receiving heart rate data of the user, andwherein the biometric sensor data comprises the heart rate data for theuser.
 19. The system of claim 12, wherein the biometric data devicecomprises ear buds, wherein the ear buds comprise a heart rate monitorfor receiving heart rate data of the user, and wherein the biometricsensor data comprises the heart rate data for the user.